Recent advances in 3D scanning technologies have brought about the capabilities to capture high-quality data at very fast speeds. Given such progress, one might consider these technologies to be on the brink of widespread dissemination. One inherent problem that must be further addressed, however, is establishing methods for 3D range data compression that are robust and offer high compression ratios; such methods will ensure efficient storage and fast, high-quality data recovery.
Currently, one conventional storage standard for a single frame of 3D geometry is the mesh format. These formats (e.g. OBJ, PLY, STL) are generic in nature and perform their tasks well, storing the coordinates of each vertex often along with connectivity information. Additional information can also be stored with the mesh such as a surface normal map and a (u,v) map. Although these formats are able to perform their task of representing a frame of 3D geometry, they also require a large amount of storage to do so. For example, a single 640×480 frame of 3D geometry, with only vertex locations and connectivity information, needs about 13 MB of space. For real-time or faster 3D capture systems that want to store or stream each single frame, this large file size becomes an issue.
Given this, other 3D range data compression techniques have been proposed. One such methodology is to encode the raw 3D data in some way such that it can be represented within a 2D image. The information stored within an image's color channels can then be used to recover and reconstruct the compressed geometry. Such approaches are able to take advantage of very well established image formats (e.g., PNG) and the infrastructure built around them.
To compress 3D geometry into a 2D image, one approach is to use the principles of virtual digital fringe projection (DFP). Using the conventions of DFP along with a virtual structured light scanner, this HoloImage approach projects fringe images upon the virtual 3D geometry and captures them virtually. The resulting captured fringe images are then packed into the image (e.g., into its color channels) along with any information that may be required to unwrap the phase map between the phase images (e.g., stair image). An additional advantage to using a virtual fringe projection system which converts raw 3D geometry into a 2D image frame is its portability to video storage and streaming.
While digital fringe projection techniques that are known to the art can be useful for generation of the representations of some three-dimensional structures based on 2D images, the existing DFP techniques still have noticeable problems with common lossy image compression algorithms such as JPEG compression. While lossy compression algorithms by their very nature introduce some errors into compressed images, the artifacts that present little or no image quality degradation in traditional compressed photographs often introduce unacceptably large errors when applied to two-dimensional images that contain encoded DFP data. This makes recording of high-resolution features of three-dimensional objects difficult because the common and highly effective lossy compression algorithms often produce errors that render high-resolution DFP data unusable in practical systems. Consequently, improvements to processes for encoding and decoding DFP data that improve the quality of the DFP for high-resolution details of an object and that maintain high quality even when heavily compressed would be beneficial to the art.